Typically, flow line detection of a mobile object such as a person or a thing is realized by associating a position of the mobile object with identification information (hereafter referred to as an ID) of the mobile object. Such association enables the mobile object to be uniquely identified and its flow line to be detected. Various techniques relating to flow line detection are proposed (for example, see Patent Literatures (PTLs) 1 and 2). Note that a flow line of a mobile object is information that, having individually identified the mobile object, indicates a path through which the mobile object moves. That is, the flow line is information indicating the moving path of the individually identified mobile object. A trajectory means a connection of position coordinates detected temporally continuously for a mobile object. Accordingly, the trajectory is interrupted when the position coordinate detection for the mobile object is interrupted.
A mobile object tracking system described in PTL 1 includes a monitoring camera for taking photographs in a space and an IC tag reader. The mobile object tracking system described in PTL 1 obtains a position coordinate of each mobile object based on the output of the monitoring camera, and manages first identification information (camera tracking ID) unique to the mobile object and the position coordinate of the mobile object in a tracking information table in association with each other. The mobile object tracking system also reads unique second identification information (object ID) from each mobile object having an IC tag, and manages the second identification information and a position coordinate of the tag in a reading information table in association with each other. The mobile object tracking system further manages the first identification information, the position information, and third identification information (position management ID) in a position management table in association with each other. The mobile object tracking system described in PTL 1 also includes, for each mobile object recognized at the same time within a predetermined error range, a position estimation table for managing the second identification information and the third identification information in association with each other, and tracks each mobile object according to the position management table and the position estimation table. In detail, in the case where the position of the mobile object detected by the monitoring camera at a given time and the position at which the IC tag reader detects the object ID are within a predetermined error range, the position information of the mobile object at the given time and the object ID are managed in association with each other, in the position management table and the position estimation table. According to this structure, the mobile object tracking system described in PTL 1 tracks the mobile object by integrating detection results obtained by a plurality of sensors.
That is, the mobile object tracking system described in PTL 1 determines a mobile object recognized by first identification means (means using the camera) and a mobile object recognized by second identification means (means using the IC tag) as the same mobile object if their recognition times and recognition positions substantially match, and integrates the information obtained by the first and second identification means. This enables flow line detection for the mobile object based on position information obtained by a plurality of sensors having different detection mechanisms.
A monitoring system using a plurality of cameras described in PTL 2 has the plurality of cameras installed in a space to be monitored, and includes feature value extraction means for extracting each mobile object and its feature value information from an image of any camera through the use of an image recognition technology. The image captured by the camera and the feature value information of the mobile object are sent to mobile object comparison and tracking means via a communication network. The comparison and tracking means has, at its center, a monitor space database in which the entire space to be monitored is expressed as a three-dimensional model and also a connection of spaces where mobile objects are movable is expressed as a network, and accumulates the received feature value information. Since a plurality of mobile objects are usually present in one camera image, these mobile objects are separated each as an individual mobile object. To track separated/aggregated mobile objects, path calculation means in the comparison and tracking means calculates moving path candidates between camera photography ranges in the space to be monitored. Mobile object consistency degree calculation means in the comparison and tracking means then calculates a consistency degree between feature value sets of two mobile objects, and determines whether or not the two mobile objects match using the consistency degree. The comparison and tracking means includes personal identification information matching means for card authentication, biometric authentication, or the like installed at a door. The vicinity of the door equipped with the card authentication means is photographed by a monitoring camera to extract feature value information of an appearance, and at the same time the feature value information is associated with information of an owner by card information.
That is, the monitoring system using the plurality of cameras described in PTL 2 connects detected mobile object trajectories between monitor areas, and also associates trajectories with personal identification information by the personal identification information matching means for card authentication, biometric authentication, or the like, as a result of which flow lines with personal identification information can be created.
The monitoring system described in PTL 2 uses luminance values of human upper and lower bodies or the like, as feature values.
An image processing device capable of tracking each mobile object appearing on a video image even in the case where an occlusion occurs is described in PTL 3. The image processing device described in PTL 3 tracks each of a plurality of feature points from a previous frame to a current frame, and estimates motion of a tracking area based on the tracking result of each feature point, to specify a position of the tracking area in the current frame. The image processing device further calculates, for each feature point, reliability indicating a level of possibility that the feature point exists in the mobile object, and calculates the motion of the tracking area using the reliability. For example, the number of frames (history) in which tracking is continuously successful is used as the reliability.